code: def _get_model_components(
      self,
      model_name_path,
      use_auth_token,
      trust_remote_code,
      model_class,
  ):
      # HappyTextClassification is the only class that overwrites
      # this as we need to specify number of labels.
  
      config = AutoConfig.from_pretrained(
          model_name_path,
          use_auth_token=use_auth_token,
          trust_remote_code=trust_remote_code,
      )
      model = model_class.from_pretrained(
          model_name_path,
          config=config,
          use_auth_token=use_auth_token,
          trust_remote_code=trust_remote_code,
      )
      tokenizer = AutoTokenizer.from_pretrained(
          model_name_path,
          use_auth_token=use_auth_token,
          trust_remote_code=trust_remote_code,
      )
  
      return config, tokenizer, model
  
imports: import logging
  import os
  import tempfile
  from typing import Union
  from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
  import torch
  from transformers import AutoConfig, AutoModel, AutoTokenizer, Seq2SeqTrainer, Seq2SeqTrainingArguments, Trainer, TrainingArguments
  from happytransformer.args import EvalArgs, TrainArgs
  from happytransformer.fine_tuning_util import EvalResult, FistStep, ZERO_2_SETTINGS, ZERO_3_SETTINGS
label_desc: Use image node_id 3 for calling the HappyTransformer obj's underlying member method code with example usage: obj._get_model_components(model_name_path, use_auth_token, trust_remote_code, model_class) and returns: config,  tokenizer,  model